Building a research-to-partner pipeline

The 60-second version

The problem

An early-stage Internet of Packaging team needed to make consequential product and market decisions without a dependable recurring source of customer evidence.

I owned

Knowledge-gap workshops, research planning, survey design, recruitment, interviews, synthesis, research presentations, video communication, and product recommendations.

The pivotal decision

Turn research into a recurring product input rather than a one-time validation study, and design the program so it could also identify potential innovation and business opportunities.

What changed

Research began informing backlog and roadmap discussions, a weak market hypothesis was deprioritized, real-time temperature monitoring was validated as a meaningful need, and five packaging-business referrals were generated.

46K+

prospective research pool

135

survey responses

11

interviews

5

business opportunity referrals

My scope

Product context

The team was making product decisions before the evidence was mature

The Internet of Packaging initiative explored how connected packaging, hardware, and software could create better visibility across perishable supply chains.

Infrastructure, equipment, integrations, potential markets, and product capabilities were all still being tested at the same time.

I started by turning uncertainty into researchable questions

I ran knowledge-gap workshops with the product, technology, and commercial stakeholders. Everyone wrote down what they did not know, and we grouped those unknowns until the categories, and the disagreements inside them, were visible.

10 knowledge-gap themes

• Data
• Sensors
• Customers
• Marketing materials
• Technology

• Pricing
• Research
• Internal partnerships
• Marketplace
• Go to market

The point was not cataloging unknowns. It was agreeing on which unknowns were expensive to get wrong.

The first deliverable was not a survey. It was clarity about which assumptions were dangerous enough to investigate.

I designed a repeatable research cycle instead of a one-off study

A single validation study would have answered one question and left the team where it started. The cycle below ran against whichever gap was most dangerous at the time, and it ran again as soon as the next one surfaced.

1. {{ step.n }}

   {{ step.label }}

   {{ step.note }}

↻ Then start again at 01 with the next highest-risk gap.

01 / Decision

Design for customers' real technology environments, not an idealized stack

A connected-packaging product only works if it can meet customers where their systems actually are. So I asked them.

Research became an input to roadmap prioritization

Once evidence arrived on a recurring schedule, prioritization conversations changed shape. Instead of debating which value proposition sounded strongest, the team could look at what customers said they wanted and what they indicated they would pay for.

Evidence was useful when it changed the decision, not when it merely confirmed the roadmap.

02 / Decision

A weak market hypothesis was more valuable to stop than defend

The team had hypothesized wine as a promising vertical. Rather than letting enthusiasm turn into roadmap momentum, I used market outreach and inbound testing to look for signal first.

1. Hypothesis

   Wine could be a promising vertical for connected packaging.

2. Signal

   The wine-industry Google Ads campaign gained no impressions and was too slow to refine through approval. It was terminated.

   0 impressions from the campaign

3. Interpretation

   There was not enough evidence to justify a larger commitment to the segment.

4. Decision

   Deprioritize the market and redirect effort toward stronger segments.

The campaign plan and its results are internal documentation and are summarized here rather than reproduced. The evidence helped deprioritize the hypothesis; it did not prove that wine has no market, and other activity elsewhere in the company was outside my view.

03 / Decision

Real-time temperature monitoring kept surfacing across the supply chain

Interviews and prioritization activities kept returning the same cluster of needs: real-time visibility, traceability, and temperature control. What made it convincing was who said it.

• Truck drivers
• Logistics managers
• Sellers
• Buyers

It was still in development when the broader initiative ended. This is validated demand and a development decision, not a launched or adopted feature.

Research created value beyond the roadmap

Recruiting from a large prospective pool meant I was talking to people who ran real perishable supply chains. A study participant could contribute evidence, continue into a deeper innovation conversation, or reveal a packaging need that belonged to another Smurfit WestRock team entirely.

Where a conversation could go

1. Contribute evidence to the study
2. Open a deeper innovation conversation
3. Reveal a packaging need for another internal team, which I referred on

5

packaging-business referrals generated

These are documented business opportunity referrals. I was not involved in later sales or partnership follow-through, and what happened after the handoff is outside what I can claim.

I designed the research so the evidence could travel beyond one meeting

Findings that live in a deck nobody reopens do not change anything. Each cycle ended with the evidence packaged for the specific audience that had to act on it.

• Research readoutsFor the product team, tied to open questions
• Executive presentationsDecision-oriented, on a recurring cadence
• Short insight videosSo findings reached people who missed the meeting
• Opportunity handoffsRouted to the internal team that owned the need
• Backlog and roadmap inputEvidence attached to the items being debated

The product did not reach launch, but the research changed how decisions were made

• Customer evidence became a recurring product input
• Research informed backlog and roadmap discussions
• A weak vertical hypothesis was deprioritized before a larger investment
• Real-time temperature monitoring was validated and was in development
• Five packaging-business referrals were generated
• Executives received recurring, decision-oriented research

I would make the decision trail easier to measure next time

The program produced evidence faster than it produced a record of what that evidence changed. With the same three months again, I would build the measurement in from the start.

Early research is valuable precisely when it helps a team change direction before the cost of changing becomes high.